Predicting Closed-Loop Performance of Latent World Models: Offline Checkpoint Selection for MPC and Model-Based RL Under Non-Markovian Rewards in LunarLander
arXiv:2607.01736v1 Announce Type: new Abstract: We study how to predict the downstream closed-loop performance of a learned latent world model from validation-time diagnostics alone. Choosing the right checkpoint from a world-model training run is difficult: validation loss and multi-step prediction RMSE keep improving long after closed-loop performance has collapsed. We present a suite of structural validation-time diagnostics drawn from optimal-control theory and apply them to Gymnasium's LunarLander v3, which features shaped rewards. We train an RSSM [5, 4] world model on it and treat per c
The proliferation of complex AI models and the increasing demand for robust autonomous systems necessitate better methods for evaluating and selecting performing models beyond simple metrics.
This research addresses a critical challenge in AI development, enabling more reliable deployment of learned world models in real-world applications by improving checkpoint selection and predicting future performance.
The ability to predict closed-loop performance of latent world models from validation diagnostics improves the development cycle of model-predictive control and model-based reinforcement learning.
- · AI developers
- · Robotics companies
- · Autonomous systems integrators
- · Reinforcement learning researchers
- · Developers reliant on ad-hoc model selection
- · Systems with unreliable AI components
More efficient and reliable development of AI agents capable of operating in complex, dynamic environments.
Accelerated deployment of advanced AI in domains requiring high degrees of autonomy and predictive control, such as logistics or hazardous operations.
Increased public and industry trust in AI systems due to improved predictability and performance guarantees.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG